Reducing audio delay for mixed mode delivery of audio transmissions

ABSTRACT

Systems and methods for reducing audio delay for mixed mode delivery of audio transmissions. One example system includes a communication interface communicatively coupled to a communication network, and an electronic processor. The electronic processor is coupled to the communication interface, and configured to receive, via the communication interface, a group call request for a talkgroup. The electronic processor is configured to transmit, in response to receiving the group call request, a call grant message to a plurality of subscriber units affiliated with the talkgroup. The electronic processor is configured to determine, using a machine learning algorithm, a subset of the plurality of subscriber units based on a call grant response time, and, while a transmission hold-off timer has not expired, when a call grant response has been received from each of the subscriber units of the subset, forward an audio transmission to the subset based on the group call request.

BACKGROUND OF THE INVENTION

Radio communications are used for a variety of reasons and situations.For example, radio communications may be used by first responders, eventorganizers, and others where it is necessary to communicate with otherswho are often remote from one another. Many times, radio communicationsare sent simultaneously, so that a group of communication device userscan receive an audio signal or message at the same time. However, if thecommunication devices receive the transmissions at different times,whether due to the devices being connected via different networks orsimply having different audio reception speeds, then audio from nearbycommunication devices will interfere with one another. A user of acommunication device may in this instance have difficulty understandingthe audio output based on the audio signal (or transmission) due toechoes coming from nearby communication devices.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed invention, and explainvarious principles and advantages of those embodiments.

FIG. 1 is a diagram of a communication system, in accordance with someembodiments.

FIG. 2 is a diagram of a server included in the communication system ofFIG. 1, in accordance with some embodiments.

FIG. 3 is a flowchart of a method for reducing audio delay for mixedmode delivery of audio transmissions, in accordance with someembodiments.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of embodiments of the present invention.

The apparatus and method components have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present invention so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

DETAILED DESCRIPTION OF THE INVENTION

As noted above, varied timing among communication devices in the receiptof audio transmissions can be problematic. As also noted, varied timingmay be caused by a variety of conditions or factors. In the past,attempts have been made to allow a group of communication devices withpush-to-talk features to receive audio transmissions simultaneously. Oneapproach to solve the problem is simply combining these features andallowing the audio through to each communication device in a system whenthe audio reaches that device. However, under this approach, audiotransmissions will reach each communication device at different times,with no synchronization based on location or speed of the communicationdevice. Using this approach, two nearby communication devices couldoutput audio with just a slight overlap or delay to a complete echomaking it difficult for either user to hear their own audio output.

Another approach to addressing the same problem is to wait for allcommunication devices to connect before sending an audio transmission.However, in any group of communication devices, the group may consist ofsome communication devices with fast links, and other communicationdevices with slow links, and the device-connection speeds may be unknownto the network infrastructure. Because of this variance in connectionspeeds, there is difficulty in a wait-for-all-devices-to-connectapproach, as every communication device in the system would be forced towait for the slowest communication device in the system, including theexample case wherein one communication device never connects. Somecommunication devices may have extreme delays, and causing all userdevices to wait for audio to be transmitted is not an optimal solution,as it wastes valuable time, for example, time on the job. In cases wherea communication device never connects, audio transmissions are neverreceived.

Certain embodiments provide, among other things, reduced audio delay formixed mode delivery of audio transmissions by, in one example, utilizingmachine learning and call grant response time data to develop subsets ofusers of communication devices. Machine learning may involve usingclustering algorithms from unsupervised machine learning, such ask-means (with weighted evaluations), to cluster devices (for example,subscriber units) based on their response time. Devices within a clusterwould have similar response times. For example, the system wouldautomatically learn and put devices with similar response timestogether, creating at least a fast response cluster, a medium responsecluster, and a slow response cluster. The slow response cluster may be,for example, devices with satellite connections. In some embodiments,the PTT Server could try to synchronize voice for each clusterseparately. Certain embodiments also update the call grant response timedata based on a plurality of historical subscriber unit response times,subscriber unit location data, and subscriber unit user metadata. Forexample, utilizing machine learning, a call grant response time for aparticular device may change over time, based on the history of locationdata, subscriber unit user metadata, and response times for that device.

Some embodiments determine a plurality of weighted historical subscriberunit response times based on a received time for each of the pluralityof historical subscriber unit response times and update the call grantresponse time based on the plurality of weighted historical subscriberunit response times. For example, when looking at the history oflocation data, subscriber unit user metadata, and response times, amachine learning algorithm may weigh more recent data more heavily thanolder data, as more recent data may be more representative of thedevices actual current response time. Additionally, the machine learningalgorithm may assign a weight to the relative distance from where a datapoint was taken to the device's current location. These and otherembodiments increase the speed of communications, while reducingundesirable effects of delayed communications, for example audioechoing. Thus, certain embodiments described herein provide moreefficient use of communication system resources and improved operationof the communication systems for users, for example, public safetypersonnel.

One example embodiment provides a system for reducing audio delay formixed mode delivery of audio transmissions. The system includes acommunication interface configured to be communicatively coupled to acommunication network and an electronic processor. The electronicprocessor is coupled to the communication interface, and configured toreceive, via the communication interface, a group call request for atalkgroup. The electronic processor is configured to transmit, inresponse to receiving the group call request, a call grant message to aplurality of subscriber units, the plurality of subscriber unitsaffiliated with the talkgroup. The electronic processor is configured todetermine, using a machine learning algorithm, a subset of the pluralityof subscriber units based on a call grant response time. The electronicprocessor is configured to, while a transmission hold-off timer has notexpired, when a call grant response has been received from each of thesubscriber units of the subset, forward an audio transmission to thesubset based on the group call request.

Another example embodiment provides a method for reducing audio delayfor mixed mode delivery of audio transmissions. The method includesreceiving, via a communication interface communicatively coupled to acommunication network, a group call request for a talkgroup. The methodincludes transmitting, in response to receiving the group call request,a call grant message to a plurality of subscriber units, the pluralityof subscriber units affiliated with the talkgroup. The method includesdetermining, with an electronic processor using a machine learningalgorithm, a subset of the plurality of subscriber units based on a callgrant response time. The method includes, while a transmission hold-offtimer has not expired, forwarding an audio transmission to the subsetbased on the group call request when a call grant response has beenreceived from each of the subscriber units of the subset.

For ease of description, some or all of the example systems presentedherein are illustrated with a single exemplar of each of its componentparts. Some examples may not describe or illustrate all components ofthe systems. Other example embodiments may include more or fewer of eachof the illustrated components, may combine some components, or mayinclude additional or alternative components.

FIG. 1 illustrates an example communication system 100. Thecommunication system 100 includes a PTT server 102, a land mobile radio(LMR) network 112, a broadband network 114, and communication devices104, 106, 108, and 110. The communication devices 104, 108, 108, and 110are sometimes referred to as subscriber units. A user 116 ofcommunication device 108 may carry the communication device 108, forexample, on a belt or within a pants pocket. The user 116 is also inclose proximity to communication device 106 (for example, within abouttwenty (20) feet or less). Communication devices 104, 106, 108, and 110additionally form talkgroup 120. In this embodiment, communicationdevice 104 is connected to LMR network 112, while communication devices106, 108 and 110 are connected to broadband network 114.

The PTT server 102, described more particularly below with respect toFIG. 2, is also communicatively coupled to the two networks. A user 116may be in close proximity to multiple communication devices, such ascommunication device 106 and 108, and therefore may hear audio outputfrom both at the same time. Because of this, communication devices 106and 108 may cause an echo if their respective audio transmissions areunsynchronized, and therefore communication system 100 is set up tosynchronize audio transmissions between devices with different speedsand different networks to reduce echoes.

FIG. 1 illustrates a graphical representation of an example talkgroup120 that includes the communication devices 106 through 110. Althougheach communication device 106 through 110 is shown affiliated withtalkgroup 120, devices 106 through 110 may be associated with one ormore other talkgroups. In addition, throughout the followingdescription, reference is made to the talkgroup 120 and thecommunication devices 106 through 110 to provide examples of the methodsand systems being explained. The talkgroup 120 is merely exemplary andhas been simplified for the sake of explanation. In some embodiments,the communication system 100 includes more or fewer communicationdevices and more or fewer talkgroups. In some embodiments, thetalkgroups have more or fewer affiliated communication devices. In someembodiments, the talkgroups do not have the same number of affiliatedcommunication devices. While FIG. 1 illustrates four communicationdevices 104, 106, 108, and 110, it should be understood that embodimentsof the method 300 may be used to reduce audio delay for morecommunication devices and talkgroups than illustrated.

As described in more detail below with respect to FIG. 3, the PTT server102 is configured to receive a group call request for a talkgroup fromone of the LMR network 112 or the broadband network 114. The servertransmits the call grant message to each of the communication devices104, 106, 108, and 110 via the electronic processor 204 (which isdescribed in greater detail with respect to FIG. 2). The electronicprocessor 204 of the PTT server 102 then determines using, for example,a machine learning algorithm, a subset of the plurality of communicationdevices 104, 106, 108, and 110. In some embodiments, the machinelearning algorithm is an unsupervised machine learning algorithm. Whilea transmission hold-off timer has not expired, the electronic processor204 forwards an audio transmission to the subset when each of the subsetof the communication devices 104, 106, 108, and 110 has responded to thegroup call request. A transmission hold-off timer can be pre-configuredor learned from the same unsupervised machine learning algorithmdescribed above. The output of the machine learning algorithm includesthe clusters and the average response time delay of the clusters, whichcan be used to determine the length of the transmission hold-off timer.

FIG. 2 schematically illustrates the PTT server 102 in more detail. Inthe example provided, the PTT server 102 includes an electronicprocessor 204, a storage device 206, and a communication interface 208.The electronic processor 204, the storage device 206, and thecommunication interface 208 communicate over one or more communicationlines or buses. Wireless connections or a combination of wired andwireless connections are also possible.

The electronic processor 204 may include a microprocessor,application-specific integrated circuit (ASIC), field-programmable gatearray, or another suitable electronic device. The electronic processor204 obtains and provides information (for example, from the storagedevice 206 and/or the communication interface 208), and processes theinformation by executing one or more software instructions or modules,capable of being stored, for example, in a random access memory (“RAM”)area of the storage device 206 or a read only memory (“ROM”) of thestorage device 206 or another non-transitory computer readable medium(not shown). The software can include firmware, one or moreapplications, program data, filters, rules, one or more program modules,and other executable instructions. The electronic processor 204 isconfigured to retrieve from the storage device 206 and execute, amongother things, software related to the control processes and methodsdescribed herein.

The storage device 206 can include one or more non-transitorycomputer-readable media, and may include a program storage area and adata storage area. The program storage area and the data storage areacan include combinations of different types of memory, as describedherein. In the embodiment illustrated, the storage device 206 stores,among other things, instructions for the processor to carry out themethod of FIG. 3, and historical data about the communication devices104, 106, 108, and 110 of each network 112, and 114 (both described indetail below).

The communication interface 208 may include a transceiver (for example,a Wi-Fi or Ethernet transceiver) for communicating over one or morewired or wireless communication networks (for example, the land mobileradio (LMR) network 112 or the broadband network 114) or connections.

In some embodiments, the PTT server 102 performs machine learningfunctions, as described above. Machine learning generally refers to theability of a computer program to learn without being explicitlyprogrammed. In some embodiments, a computer program (for example, alearning engine) is configured to construct an algorithm based oninputs. Supervised learning involves presenting a computer program withexample inputs and their desired outputs. The computer program isconfigured to learn a general rule that maps the inputs to the outputsfrom the training data it receives. Example machine learning enginesinclude decision tree learning, association rule learning, artificialneural networks, classifiers, inductive logic programming, supportvector machines, clustering, Bayesian networks, reinforcement learning,representation learning, similarity and metric learning, sparsedictionary learning, and genetic algorithms. Using all of theseapproaches, a computer program can ingest, parse, and understand data,and progressively refine algorithms for data analytics.

As noted, audio delay caused by mixed mode delivery of audiotransmissions can create echoes between nearby communication devices.Accordingly, FIG. 3 illustrates a method 300 for reducing audio delayfor mixed mode delivery of audio transmissions. The method 300 isdescribed as being performed by the PTT server 102 and, in particular,the electronic processor 204. However, it should be understood that insome embodiments, portions of the method 300 may be performed by otherdevices, including for example, the storage device 206 or communicationinterface 208.

As illustrated in FIG. 3, at block 302, the electronic processor 204receives a group call request from one of the LMR network 112 or thebroadband network 114. For example, a group call request may be arequest from one of the communication devices to send an audiotransmission to the communication devices in that talkgroup. In responseto this, at block 304, the electronic processor 204 transmits a callgrant message to each radio in a talkgroup consisting of a plurality ofcommunication devices. The talkgroup may include communication devicesfrom both the LMR network 112 and the broadband network 114, and each ofthe communication devices may operate and respond to the group callrequest at varying speeds.

At block 306, the electronic processor 204 determines a subset of theplurality of communication devices. Determination of the subset of theplurality of communication devices may include machine learning, and thesubset of communications devices may be a group of communication devicesdetermined to respond to the group call request at faster speeds thanother communication devices of the plurality. In some embodiments,determination of the subset includes the electronic processor 204updating call grant response times using a machine learning algorithmbased on at least on historical subscriber unit response times,subscriber unit location data, and subscriber unit user metadata.Historical subscriber unit response times may be response times for aspecific communication device to previous call grant requests.Subscriber unit location data may be GPS coordinate location or othertracking data for the location of a communication device, and subscriberunit user metadata may be a priority level of a communication device.

In some embodiments, these updates to the call grant response times maybe weighted, for example a more recent call response time may be weighedmore than a call grant time from a month ago when determining the callgrant response time for that communication device. Similarly, subscriberunit location data that is more recent may be weighed more heavily thanprevious location data, or in some embodiments may be the only locationdata taken into consideration when determining the call grant responsetime for that communication device. In some embodiments, when multiplesubscriber units are within a certain proximity (for example, less than20 yards away), the machine learning algorithm may use this informationfor clustering the subscriber units and creating the subset ofsubscriber units. In some embodiments, the electronic processor 204 maywait until a response is received from all radios in that proximitybefore transmitting an audio signal.

At block 308, the electronic processor 204 checks to see if there is aresponse from all of the communication devices of the subset. If thereis a response from all of the subset of the communication devices, thenthe electronic processor 204 proceeds to block 312, where it forwards anaudio transmission to the subset. If there is not a response from all ofthe subset of the communication devices, then the electronic processor204 proceeds to block 310, where the electronic processor checks to seeif a transmission hold-off timer has expired. If a transmission hold-offtimer has expired, then the electronic processor proceeds to block 312and forwards the audio transmission to the subset of the communicationdevices.

In one embodiment, if a transmission hold-off timer expires before allof the communication devices of the subset respond, then the audiotransmission is sent regardless of not having a response from all of thecommunication devices of the subset. In some embodiments, the subscriberunit user metadata includes at least one critical user identifier (forexample, a unique alphanumeric identifier associated with a user of thesubscriber unit), which identifies a user who should receive the audiotransmission. For example, a supervisor commanding an incident responseteam may be considered a critical user for audio transmissions directedto a talkgroup for the incident response team. In such embodiments, theelectronic processor 204 is configured to hold the audio transmissionuntil a call grant response has been received from a subscriber unitassociated with the at least one critical user identifier, regardless ofthe transmission hold-off timer status. If the timer has not expired,then the electronic processor resets to block 306 and proceeds tore-determine a subset of the plurality of communication devices.

In some embodiments, the electronic processor 204 will forward the audiotransmission while a hold-off timer has not expired when a call grantresponse has been received from each of a minimum threshold ofsubscriber units from the subset, rather than waiting for every user inthe subset. In another embodiment, the electronic processor 204 willforward the audio transmission while a hold-off timer has not expiredwhen a call grant response has been received from each of the identifiedcritical users of the subset. In still other embodiments, when theelectronic processor 204 receives a call grant response from acommunication device of the subset after the call grant response timehas expired, the electronic processor 204 forwards the audiotransmission in an unsynchronized fashion to the communication device.

In the foregoing specification, specific embodiments have beendescribed. However, one of ordinary skill in the art appreciates thatvarious modifications and changes can be made without departing from thescope of the invention as set forth in the claims below. Accordingly,the specification and figures are to be regarded in an illustrativerather than a restrictive sense, and all such modifications are intendedto be included within the scope of present teachings.

The benefits, advantages, solutions to problems, and any element(s) thatmay cause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeatures or elements of any or all the claims. The invention is definedsolely by the appended claims including any amendments made during thependency of this application and all equivalents of those claims asissued.

Moreover in this document, relational terms such as first and second,top and bottom, and the like may be used solely to distinguish oneentity or action from another entity or action without necessarilyrequiring or implying any actual such relationship or order between suchentities or actions. The terms “comprises,” “comprising,” “has,”“having,” “includes,” “including,” “contains,” “containing” or any othervariation thereof, are intended to cover a non-exclusive inclusion, suchthat a process, method, article, or apparatus that comprises, has,includes, contains a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus. An element proceeded by“comprises . . . a,” “has . . . a,” “includes . . . a,” or “contains . .. a” does not, without more constraints, preclude the existence ofadditional identical elements in the process, method, article, orapparatus that comprises, has, includes, contains the element. The terms“a” and “an” are defined as one or more unless explicitly statedotherwise herein. The terms “substantially,” “essentially,”“approximately,” “about” or any other version thereof, are defined asbeing close to as understood by one of ordinary skill in the art, and inone non-limiting embodiment the term is defined to be within 20%, inanother embodiment within 10%, in another embodiment within 2% and inanother embodiment within 1%. The term “coupled” as used herein isdefined as connected, although not necessarily directly and notnecessarily mechanically. A device or structure that is “configured” ina certain way is configured in at least that way, but may also beconfigured in ways that are not listed.

It will be appreciated that some embodiments may be comprised of one ormore generic or specialized processors (or “processing devices”) such asmicroprocessors, digital signal processors, customized processors andfield programmable gate arrays (FPGAs) and unique stored programinstructions (including both software and firmware) that control the oneor more processors to implement, in conjunction with certainnon-processor circuits, some, most, or all of the functions of themethod and/or apparatus described herein. Alternatively, some or allfunctions could be implemented by a state machine that has no storedprogram instructions, or in one or more application specific integratedcircuits (ASICs), in which each function or some combinations of certainof the functions are implemented as custom logic. Of course, acombination of the two approaches could be used.

Moreover, an embodiment can be implemented as a computer-readablestorage medium having computer readable code stored thereon forprogramming a computer (for example, comprising a processor) to performa method as described and claimed herein. Examples of suchcomputer-readable storage mediums include, but are not limited to, ahard disk, a CD-ROM, an optical storage device, a magnetic storagedevice, a ROM (Read Only Memory), a PROM (Programmable Read OnlyMemory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM(Electrically Erasable Programmable Read Only Memory) and a Flashmemory. Further, it is expected that one of ordinary skill,notwithstanding possibly significant effort and many design choicesmotivated by, for example, available time, current technology, andeconomic considerations, when guided by the concepts and principlesdisclosed herein will be readily capable of generating such softwareinstructions and programs and ICs with minimal experimentation.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

We claim:
 1. A system to reduce audio delay for mixed mode delivery ofaudio transmissions, the system comprising: a communication interfaceconfigured to be communicatively coupled to a communication network; andan electronic processor, coupled to the communication interface, andconfigured to receive, via the communication interface, a group callrequest for a talkgroup, transmit, in response to receiving the groupcall request, a call grant message to a plurality of subscriber units,the plurality of subscriber units affiliated with the talkgroup,determine a subset of the plurality of subscriber units based on a callgrant response time; and while a transmission hold-off timer has notexpired, when a call grant response has been received from each of thesubscriber units of the subset, forward an audio transmission to thesubset based on the group call request.
 2. The system of claim 1,further comprising: a second communication interface configured to becommunicatively coupled to a second communication network; wherein theplurality of subscriber units is connected to the second communicationnetwork; and wherein the electronic processor is coupled to the secondcommunication interface, and further configured to transmit the callgrant message to the plurality of subscriber units via the secondcommunication interface.
 3. The system of claim 1, wherein theelectronic processor is further configured to, when the transmissionhold-off timer has expired, forward the audio transmission to the subsetbased on the group call request.
 4. The system of claim 1, wherein theelectronic processor is further configured to update the call grantresponse time based on at least one selected from the group consistingof a plurality of historical subscriber unit response times, subscriberunit location data, and subscriber unit user metadata.
 5. The system ofclaim 4, wherein the electronic processor is further configured todetermine a plurality of weighted historical subscriber unit responsetimes based on a received time for each of the plurality of historicalsubscriber unit response times; and update the call grant response timebased on plurality of weighted historical subscriber unit responsetimes.
 6. The system of claim 4, wherein the subscriber unit usermetadata includes at least one critical user identifier, and wherein theelectronic processor is further configured to hold the audiotransmission until a call grant response has been received from asubscriber unit associated with the at least one critical useridentifier, regardless of the transmission hold-off timer status.
 7. Thesystem of claim 1, wherein the electronic processor is furtherconfigured to, while a transmission hold-off timer has not expired, whena call grant response has been received from each of a minimum thresholdof subscriber units of the subset, forward the audio transmission. 8.The system of claim 1, wherein the electronic processor is furtherconfigured to receive a call grant response from a subscriber unit ofthe subset after the call grant response time has expired, and forwardthe audio transmission in an unsynchronized fashion to subscriber unit.9. The system of claim 2, wherein the first communication network is aland mobile radio network and the second communication network is abroadband network.
 10. The system of claim 1, wherein the electronicprocessor is configured to determine the subset of the plurality ofsubscriber units using a machine learning algorithm.
 11. A method forreducing audio delay for mixed mode delivery of audio transmissions, themethod comprising: receiving, via a communication interfacecommunicatively coupled to a communication network, a group call requestfor a talkgroup, transmitting, in response to receiving the group callrequest, a call grant message to a plurality of subscriber units, theplurality of subscriber units affiliated with the talkgroup,determining, with an electronic processor using a machine learningalgorithm, a subset of the plurality of subscriber units based on a callgrant response time; and while a transmission hold-off timer has notexpired, forwarding an audio transmission to the subset based on thegroup call request when a call grant response has been received fromeach of the subscriber units of the subset.
 12. The method of claim 11,wherein transmitting the call grant message to the plurality ofsubscriber units includes transmitting the call grant message to asecond communication network via a second communication interface. 13.The method of claim 11, further comprising: forwarding the audiotransmission to the subset based on the group call request when thetransmission hold-off timer has expired.
 14. The method of claim 11,further comprising: updating the call grant response time based on atleast one selected from the group consisting of a plurality ofhistorical subscriber unit response times, subscriber unit locationdata, and subscriber unit user metadata.
 15. The method of claim 14,further comprising: determining a plurality of weighted historicalsubscriber unit response times based on a received time for each of theplurality of historical subscriber unit response times; and updating thecall grant response time based on plurality of weighted historicalsubscriber unit response times.
 16. The method of claim 14, furthercomprising: holding the audio transmission until a call grant responsehas been received from a subscriber unit associated with at least onecritical user identifier, regardless of the transmission hold-off timerstatus.
 17. The method of claim 11, further comprising: while atransmission hold-off timer has not expired, forwarding the audiotransmission when a call grant response has been received from each of aminimum threshold of subscriber units of the subset.
 18. The method ofclaim 11, further comprising receiving a call grant response from asubscriber unit of the subset after the call grant response time hasexpired, and forwarding the audio transmission in an unsynchronizedfashion to subscriber unit.
 19. The method of claim 12, whereinreceiving a group call request includes receiving a group call requestfrom a land mobile radio network; and transmitting the call grantmessage to a second communication network includes transmitting the callgrant message to a broadband network.
 20. The method of claim 11,determining a subset of the plurality of subscriber units based on acall grant response time includes determining a subset of the pluralityof subscriber units using a machine learning algorithm.